ABSTRACT
Landslides are widely spread natural calamity on Earth and is a general term used to describe the down-slope of soil, rock and organic material under the influence of gravity. This chapter proposes a novel method for landslide assessment with the help of input variables that have direct physical significance, using a Novel Neuron Model Approach that works in complex domain and is an extension of the self-look up table approach based Counterpropagation neuron model. There are a number of potentially damaging natural phenomenon, which are termed ‘geo-hazards’ and slope instability is one of them. Landslide hazard assessment of existing landslide areas, in which the most probable patterns and mechanisms of future movements are the recurrence of past patterns and mechanisms, is often seen as requiring the recognition of those patterns. The chapter describes the network architecture of the ComplexValued Counterpropagation Network and shows the evaluation methodology used to process the geological parameters.
